Higher education institutions are complex enterprises with missions that are difficult to quantify, and often support activities that mirror that of a small city. In good times higher education leadership often depended on experience and judgement to make decisions. Higher education, both as an industry and as individual institutions, is now under substantial pressure to justify value, and to convince a wide range of stakeholders that they are operating in an effective and efficient manner.
Institutions are struggling as never before and are faced with new and difficult decisions—will developing online learning programs be a profitable way to support traditional education?; should foreign language programs be consolidated?; should the program that looked so promising in the ‘90s be cut back?; are there duplications in supporting services across the campus that might be consolidated into a central shared service?; would it be better to outsource support for an activity or continue it on-campus? This is just a cross-sectional flavor of discussions going on across higher education at this time.
"When major decisions filter up to the Board of Regents they will have a common framework for considering and making final approval decisions"
When faced with similar questions, a for-profit enterprise would generally ask early on, “what are the full or complete costs to do X, and what would be the implications or outcomes?” From our experience, most higher ed institutions don’t do that, largely because the decision makers don’t have good data and tools to systematically combine data compiled for different objectives to answer the question in a reliable manner. In considering a question such as what does it cost to offer a new course—or a new program—decision makers likely can identify the direct costs of faculty and graduate assistants’ time, but the indirect costs of library resources, space, IT needs, electricity, HVAC, parking, student support services, etc., are generally perceived as sunk costs and given little consideration, if any at all. That may work in the short term, but from an institutional perspective these add up over time. There is a common perception among external stakeholders that higher education has consequentially allowed the creation of “administrative bloat,” unrelated to the teaching and learning mission. Most within higher education understand that successful teaching and learning does not exist in a vacuum but requires many ancillary activities and services. That said, these are often not part of the initial, or subsequent, decision processes.
How can higher education, as an industry, change this to make decisions more business-like? One approach would be to develop financial models that capture a wide range of direct and indirect costs for activities. To this suggestion, however, in response a Business Officer might say, “To some extent we are already doing this. If the President or the Board has a question, we have our analysts collect as much data as we can find and develop a spreadsheet to answer the question.”
That is not what we are proposing—a series of one-off evaluations or analyses, rather we are promoting a more holistic and systemic approach that takes into account a wider array of costs and requirements. Another Business Officer might respond then, “What you are proposing is Activity Based Costing (ABC), which was developed in and for the manufacturing industry. We aren’t building widgets and the overhead of developing such a model isn’t worth the ROI.” That might historically have been true, but with the digitization of much data at an institution and modern systems, this is no longer the case. One can develop a useful financial model, that might be imprecise initially, but by applying it transparently across the institution, over time it will become a critical decision support tool.
This is the premise of the book, Reengineering the University: How to be Mission Centered, Market Smart, and Margin Conscious, by Dr. Bill Massy. In this book, he draws on the experience of Australian Universities that adopted this approach starting in 2006.They have evolved the model into a useful decision support tool. The University of California Riverside has similarly invested in such modeling. In the University System of Maryland, the flagship institution, University of Maryland College Park (UMCP), is a year into modeling the answer to the question, “what does it cost to offer a course?” They are just beginning to get value from the effort. Having an accepted model will subsequently allow the institution to develop scenarios around the question, “what would it cost to offer a course, or program, in X?”
Note that these examples are focused on academic questions. There are several justifications for this—teaching and learning are the main mission of most institutions and consume the majority of institutional resources; academic data has long been collected for reporting purposes and standard, widely accepted definitions are in place; thus developing a model for basic academic questions appears to give the biggest bang for the buck as a starting place. That said, in other industries models have been developed to support decision making in a wide range of operational areas.
While it is still in formation, the plan for the University System of Maryland (USM) is to take the basic financial model developed by UMCP and transfer it, perhaps in a ‘lite version’ to less complex institutions. This use of “Systemness” has many potential advantages. Most obvious, it builds on the heavy lifting that UMCP undertook in developing the model in the first place. Additionally, to the extent possible, it allows a degree of normalization in decision making and communication across the System. When major decisions filter up to the Board of Regents they will have a common framework for considering and making final approval decisions. Further, best practices at one institution can be evaluated and potentially adopted at another.
The complexity of this effort is not lost on the proposers. There are the ABC doubters, who have an historic mental image of the process. Additionally, the success of the initiative depends on active participation by a wide range of institutional stakeholders faculty, staff, and senior administrators. It relies on open transparency of institutional data and decision making, which may not be supported by all stakeholders. Finally, it requires acceptance of the institutional view of data as opposed to locally held data. In the end, initiatives such as this require support from the top and engagement at all levels of the institution.
An initiative such as this might have been viewed as overkill in higher education during times of growth and financial stability. Demographics indicate that the time of growth is coming to an end, and financial stability is at best a hope for many institutions. It is perhaps time to start putting in place the tools for more business-like decision making.